​While it is clear that, with appropriate practice, people will improve on most perceptual tasks, the improvements seen from such practice commonly fail generalize to new tasks. However, while this type of learning specificity is undoubtedly the most frequently described outcome of perceptual training, over the past few decades, numerous examples have been described in the literature wherein perceptual learning does generalize beyond the confines of the trained task. Here I’ll discuss recent work from my lab showing that such generalization can take (at least two) distinct functional forms. The first form, which we have dubbed “immediate transfer,” describes cases wherein training on some Task A produces immediate benefits when performing some new Task B. The second form, which we refer to as “learning to learn,” describes cases wherein training on some Task A produces no immediate benefit when performing some new Task B, but instead allows Task B to be learned more rapidly than it would have otherwise. In my talk, I’ll discuss the methods we have used to produce these two forms of generalization experimentally, the statistical techniques we have used to distinguish them from one another, how these patterns speak to the question of what exactly what learned via training, and finally consider how this distinction may call for a reinterpretation of our previous work examining the consequences of action video game playing on perceptual abilities.